Introduction to Recommender Systems

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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4680 reviews

Course Description

This course introduces the concepts, applications, algorithms, programming, and design of recommender systems--software systems that recommend products or information, often based on extensive personalization. Learn how web merchants such as Amazon.com personalize product suggestions and how to apply the same techniques in your own systems!
Reviews 8/10 stars
8 Reviews for Introduction to Recommender Systems

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Nikhil sarpotdar profile image
Nikhil sarpotdar profile image
8/10 starsCompleted
  • 4 reviews
  • 4 completed
5 years, 3 months ago
This is a great course. I did not have any background in the material and it was fund to learn a lot of the concepts. If you know basic linear algebra (and little bit of vector math) this class shouldn't be too difficult. Joe Konstan has been in this field for a long time and is well known in the field (a lot of papers reference joe's papers). The workload is on a larger side, which is true for most of the computer science courses as well. A distinction about this MOOC was we were taking it in parallel with university of Minnesota students taking it for credit at their school. As a result the course was setup to be a normal "university level course" together with timed midterms and final exams and a lot of written and programming assignments. Studying for the exams and all the assignments took up a lot of my time (the course itself is not hard to follow/understand) I have the following suggestions for improvements: 1) De-couple lenks... This is a great course. I did not have any background in the material and it was fund to learn a lot of the concepts. If you know basic linear algebra (and little bit of vector math) this class shouldn't be too difficult. Joe Konstan has been in this field for a long time and is well known in the field (a lot of papers reference joe's papers). The workload is on a larger side, which is true for most of the computer science courses as well. A distinction about this MOOC was we were taking it in parallel with university of Minnesota students taking it for credit at their school. As a result the course was setup to be a normal "university level course" together with timed midterms and final exams and a lot of written and programming assignments. Studying for the exams and all the assignments took up a lot of my time (the course itself is not hard to follow/understand) I have the following suggestions for improvements: 1) De-couple lenksit from the assignments. I did all my assignments in R. The DB is fairly small that it can be easily done in R. Also structures dealing with matrices and dot products and vector magnitudes should be done in R (which is optimized for it) than java. If you cannot/do not want to decouple lenskit please add one more week of instructions to go over java concepts and lenskit concepts (which you already do) that will be needed in the course. There is no need to know java. People knowing any programming language can take this course. 2) The course is all encompassing overview of the the field, combining technical aspects together with business considerations. I appreciated that very much. Whats the point of a recommender system that is technically sound but doesn't do a good job of helping a business meet its need ( i.e RMSE is low but lift is low as well). I feel however there was too much static text on the slides. the slides can be made better to have more figures, more movement etc. I appreciate the fact that in some of the videos the professors derived the formula on screen with hand. That made it less mundane. 3) Finally it seemed that the class needs to be freshened up. While we did have a lot of interviews with experts in the field (I particularly like the interview with Netflix and LinkedIn since that is a contemporary use of the recommender system by companies to actually sell their products and make money) it seemed that we were reading papers and learning algorithms that were 10 years old now. Should we be learning something that is more contemporary? Thanks a lot nevertheless. I enjoyed the class a lot and appreciate your efforts (obviously a lot of it) in putting the class together. I learned a lot as well an would recommend it to anyone interested in learning about recommender systems.
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Mario Gómez Martínez profile image
Mario Gómez Martínez profile image
9/10 starsCompleted
  • 3 reviews
  • 3 completed
5 years ago
I really enjoyed taking this course. Format & Contents: For an introductory course it was quite extense and detailed. The course described all the major paradigms in recommender systems, spiced with plenty of extra material, mostly in the form of interviews with academy and industry experts in the field. Material: The qualtiy of the presentation was fine, the style was rather classic, not funny but professional, definetely plenty of interesting and useful content, but often long and demanding. The complementary material was also interesting (some reviews and other papers). Assessment: One of the most thorough assessment systems I have seen so far in a MOOC, especially for the intense use of peer-to-peer assessment. In general I think both the quizzess and the programming assignments were appropiate. It is possible to pass the course while not taking the programming assignments, although I strongly recommend the programming part of it... I really enjoyed taking this course. Format & Contents: For an introductory course it was quite extense and detailed. The course described all the major paradigms in recommender systems, spiced with plenty of extra material, mostly in the form of interviews with academy and industry experts in the field. Material: The qualtiy of the presentation was fine, the style was rather classic, not funny but professional, definetely plenty of interesting and useful content, but often long and demanding. The complementary material was also interesting (some reviews and other papers). Assessment: One of the most thorough assessment systems I have seen so far in a MOOC, especially for the intense use of peer-to-peer assessment. In general I think both the quizzess and the programming assignments were appropiate. It is possible to pass the course while not taking the programming assignments, although I strongly recommend the programming part of it. Note than any computer language can be used, as far as one is able to implement the required algorithms. Overall: All in all I highly recommend this course, and I strongly recommend taking also the programming track. It is a very demanding course, challenging at some points, but the material is great and with effort there is much to learn from it.
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Jonas Barnett profile image
Jonas Barnett profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 3 months ago
This is by no means an easy course if you want to do more than the concepts stream. But I was glad it wasn't made out to be an easy course with an easy way to certification. The course was run concurrently for UMN students as well so you've got to take into account that this is a university grade course. The video lectures were long as some people have commented but there is simply a lot of material to cover. I didn't find it slow because it is interesting to understand some of the background and the problems that pioneers encountered. I liked the fact that the course was not dumbed-down. I also liked the wide variety of interviews with other experts and practitioners in the field. On balance, I think the course worked well. There were some execution glitches for the peer grading work - we needed more guidance as to the nuances of a poor, mediocre, good and excellent answer but that part should improve if they choose to run the cours... This is by no means an easy course if you want to do more than the concepts stream. But I was glad it wasn't made out to be an easy course with an easy way to certification. The course was run concurrently for UMN students as well so you've got to take into account that this is a university grade course. The video lectures were long as some people have commented but there is simply a lot of material to cover. I didn't find it slow because it is interesting to understand some of the background and the problems that pioneers encountered. I liked the fact that the course was not dumbed-down. I also liked the wide variety of interviews with other experts and practitioners in the field. On balance, I think the course worked well. There were some execution glitches for the peer grading work - we needed more guidance as to the nuances of a poor, mediocre, good and excellent answer but that part should improve if they choose to run the course again. I did the NLP course by Columbia University earlier in the year and in comparison, I think they are equal in terms of learning outcomes and difficulty but with different approaches. With the NLP course, your mark is based on the level of improvement in the NLP models you were building. The recommender system course has a more traditional approach to assessment.
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vaggelas profile image
vaggelas profile image
6/10 starsDropped
  • 28 reviews
  • 26 completed
5 years, 5 months ago
This was one of the few courses that i was pretty excited on the beginning and i dropped it at the end. Topic is kind of unique, tons of stuff in the lectures , professor seems very proficient but... This was n't a very good Mooc,maybe it s a amazing course in College,but as a mooc i think it is n't well structured.Lectures were tooo long,the talks kind of slow, the desing of the video lectures were the best. I really hope that this course will become better in the next offering because i want to complete it,i want to learn all this useful knoledge that the course provides you.
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Alfonso profile image
Alfonso profile image
10/10 starsCompleted
  • 3 reviews
  • 2 completed
5 years, 4 months ago
This is definitely the best MOOC I have taken (I have completed 2 other courses and I am following one other): the lecture videos were of the highest quality, the instructors were very good, the exercises were varied and well thought, etc. I have enjoyed following the course and I feel I have learnt a lot. The course offered two tracks: concepts and programming (same as concept but it included programming assignments). I did not have time to do the programming assignments, so I just followed the concept track. My estimate of the my workload is 5-7 hours per week. I am thankful to the instructors to have offered this course and I highly recommend it to anyone interested in recommender systems.
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Roman Shapovalov profile image
Roman Shapovalov profile image
6/10 starsCompleted
  • 7 reviews
  • 7 completed
5 years, 3 months ago
This is quite a practical course on the recommender systems design. The lecturers seem to be nice persons who are close to applications. However, the lectures may benefit from more brevity and mathematical strictness. Mistakes are quite common. Overall, most concepts are too simple to spend that much time on them. The strong side are the interview lectures: the hosts invite some people from industry to tell about the systems they designed, which gives a clue on what is really useful in practice. Tests generally help to control yourself, though the options are sometimes quite vague. Peer-graded assignments are good exercises, but they lack a good grading system: there is no field for textual feedback, so you cannot point out to the problems / possible improvements in your peer’s work, and thus don’t get any explanation of your grade. However, reading about peers’ projects is quite fun. The weakest part of the course IMHO is the progra... This is quite a practical course on the recommender systems design. The lecturers seem to be nice persons who are close to applications. However, the lectures may benefit from more brevity and mathematical strictness. Mistakes are quite common. Overall, most concepts are too simple to spend that much time on them. The strong side are the interview lectures: the hosts invite some people from industry to tell about the systems they designed, which gives a clue on what is really useful in practice. Tests generally help to control yourself, though the options are sometimes quite vague. Peer-graded assignments are good exercises, but they lack a good grading system: there is no field for textual feedback, so you cannot point out to the problems / possible improvements in your peer’s work, and thus don’t get any explanation of your grade. However, reading about peers’ projects is quite fun. The weakest part of the course IMHO is the programming assignments. They typically provide practice not to learn the algorithms, but to learn the API of LensKit, the Java framework created in the lecturers’ lab. That is an enterprise-style system with dependency injection and database access objects, so I typically spent a lot of time figuring out dependencies between classes and routines to transform different kinds of immutable vectors to write several lines that actually solve the problem. It was fun to test the algorithms on real data (movie ratings collected from the course students), but the downside was that computation could last an hour or more. Overall, the course was useful for me, though it could be more useful given the amount of time it took. I have some background in machine learning, so the course looked to me like a set of heuristics. I think this in not the problem of this particular course; the field of recommender systems is just too young, and I hope a lot of methodology is yet to be developed.
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Andrea Barraza profile image
Andrea Barraza profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
5 years, 4 months ago
I think this is one of the best courses I have taken. It is very complete and the professors have a lot of experience in the subject. It is time demanding but worth it. One of the high points for me is that professors interviewed world known experts in every module. This allowed students to get an overview of the most important research that is being carried out on the subject. It must be understood that this subject is very dense which is why this is an introductory course. In addition, it is not a machine learning course and it shouldn't be. I loved the course and highly recommend it.
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Marjan Ček profile image
Marjan Ček profile image
9/10 starsCompleted
  • 20 reviews
  • 17 completed
5 years, 4 months ago
The course was very well structured, though I felt there was too much emphasis on evaluation tests (yet very useful in real life). I also missed some more advanced content-based methods, though other courses touch the topic in depth. The programming exercises were in Java using the LensKit library with half baked projects. That made it easier to focus in the logic of the problem you where trying to solve, but took you a step back from understanding the details of constructing a system yourself. All in all, I think the lectures are very well thought of, they cover a whole lot of useful information, they include interviews with industry big names. The course requires quite some time to do the programming and written exercises, and the exams are far from trivial, so be prepared to invest quite some time in the course. I highly recommend it to anyone interested in the topic, or thinking about implementing a recommender.
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